Some of the key themes that emerged are the polling failures in 2016 US Presidential Elections, Deep Learning, IoT, greater focus on value and ROI, and increasing adoption of predictive analytics by the "masses" of industry.

Here is what the experts thought on
Data Science, Predictive Analytics Main Developments in 2016 and Key Trends in 2017.

In 2017, we expect to see greater expansion of edge analytics use cases: machine learning embedded with sensors or close to the point of data collection -- the machine learning may be invoked via APIs or in processors close to the data collector or integrated into the sensor chip architecture itself. The patterns, trends, anomalies, and emergent phenomena (BOI: Behaviors of Interest) that are discovered close to the edge will enable better and faster predictive and prescriptive analytics applications in many domains: cybersecurity, digital marketing, customer experience, healthcare, emergency response, engine performance, autonomous vehicles, manufacturing, supply chain, and more.

Tom Davenport, Distinguished Professor at Babson College, co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.

2016 Developments

Decentralization of analytics groups: After a period of consolidation, organizations began to decentralize analytics to business units and functions, in many cases attempting to retain some degree of enterprise coordination.

Combinations of proprietary and open source technologies:
Many large corporations are making use of proprietary and open source analytics and big data technologies-often combined within a single application.

Fragmentation of cognitive technologies: Large, monolithic cognitive technologies have been broken into a series of single-function APIs that can be combined to form complete systems.

Fuzzy quantitative roles: Quantitative analysts, data scientists, and developers of cognitive applications have become less distinguishable; clear roles and titles are a thing of the past.

This year's "big data" event was the U.S. election
cycle because it brought the big data/data science/predictive analytics discussion to the Public Square. Granted, these weren't the terms most folks were using, but in the U.S., we experienced data up-close-and-personal: its' role, its' uses and misuses, its' interpretations and misinterpretations, and its' insights, right and wrong.

As data continues to pervade every aspect of our professional and personal lives, courtesy of the
Internet of Things, both the private and public sectors will be pressured to ensure that the collection, use, and analysis of data is safe, secure, and
ethical. If a company doesn't get it right, they will cease to exist.

John Elder , founder and chairman of Elder Research, US largest analytics consultancy.

A year ago, Science magazine gave a "runner-up scientific breakthrough of 2015 award" to a study that attempted to
replicate 100 top experiments published in psychology journals a few years previous. But researchers were only able to replicate 39. Bad as this is, it is feared to be much better than the track record for Epidemiology, where those published medical "discoveries" appear to be right only 5-35% of the time. Most of the problems of finding
spurious correlations, I believe, are due to bad data science.
Replacing outdated significance formulas with resampling procedures such as Target Shuffling, would better calibrate how likely random results could arise as strong as the apparent discovery, given the vast search performed by the researcher and the mining software. New criteria would be needed for publication worthiness, but results would be much more reliable, saving massive resources, and even lives.

Anthony Goldbloom, Co-founder and CEO of Kaggle, the leading Data Science competition platform.

Companies like Airbnb, Climate Corporation (now Monsanto) and Opendoor are great examples of how data science can have a big impact. They have built strong data science teams that impact decisions across their companies In 2017, we'll see those companies lead the way in adopting tools and processes that solve some of the big pain points in doing data science: particularly sharing and
collaborating on data science workflows and pushing models into production.
In 2016, the hot topics in academic research moved from deep neural networks to
reinforcement learning and generative models.

In 2017, we should start to see some of these techniques used for pragmatic business use cases. Some of the promising areas for reinforcement learning include algorithmic trading and ad targeting.